{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/feature-compatible-progressive-learning-for","title":"Feature-compatible Progressive Learning for Video Copy Detection","arxiv_id":"2304.10305","date":"2023-04-20","proceeding":null,"authors":["Wenhao Wang","Yifan Sun","Yi Yang"],"abstract":"Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content. This paper presents our second place solutions to the Meta AI Video Similarity Challenge (VSC22), CVPR 2023. In order to compete in this challenge, we propose Feature-Compatible Progressive Learning (FCPL) for VCD. FCPL trains various models that produce mutually-compatible features, meaning that the features derived from multiple distinct models can be directly compared with one another. We find this mutual compatibility enables feature ensemble. By implementing progressive learning and utilizing labeled ground truth pairs, we effectively gradually enhance performance. Experimental results demonstrate the superiority of the proposed FCPL over other competitors. Our code is available at https://github.com/WangWenhao0716/VSC-DescriptorTrack-Submission and https://github.com/WangWenhao0716/VSC-MatchingTrack-Submission.","url_abs":"https://arxiv.org/abs/2304.10305v2","url_pdf":"https://arxiv.org/pdf/2304.10305v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"feature-compatible-progressive-learning-for","repo_url":"https://github.com/wangwenhao0716/vsc-descriptortrack-submission","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"feature-compatible-progressive-learning-for","repo_url":"https://github.com/wangwenhao0716/vsc-matchingtrack-submission","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"copy-detection","task_name":"Copy Detection"},{"task_slug":"video-similarity","task_name":"Video Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.10305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.10305"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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